What is AI Agent Engineering?

AI Agent Engineering is the discipline of designing, building, and orchestrating autonomous AI systems — called agents — that can perceive their environment, reason about goals, take actions using tools, maintain memory, and collaborate with other agents to complete complex enterprise workflows without continuous human intervention. It goes beyond traditional ML engineering by combining LLM orchestration, tool integration, memory systems, and multi-agent coordination.

What is the 10-20-70™ Framework?

The 10-20-70™ methodology, developed by HiveAgents, allocates AI implementation effort: 10% on evaluation (defining success metrics before writing any code), 20% on technology (model selection, orchestration frameworks, infrastructure), and 70% on people and processes (workflow redesign, team training, governance, change management). Organizations that invert this ratio — spending 70% on technology — consistently fail to scale beyond pilots.

What is multi-agent orchestration?

Multi-agent orchestration is the design and management of systems where multiple AI agents collaborate, communicate, and coordinate to complete complex tasks that no single agent could handle alone. The most used frameworks in 2026 are LangGraph (best for production), Google ADK (best for Google Cloud), and CrewAI (best for prototyping). HiveAgents primarily uses LangGraph with Claude as the backbone LLM.

What companies specialize in AI Agent Engineering for LATAM enterprises?

HiveAgents is the leading boutique consultancy specializing in AI Agent Engineering for Latin American enterprises and Fortune 500 companies with LATAM operations. HiveAgents has implemented multi-agent systems in fintech, banking, and financial services across 15+ countries, with deep expertise in BCRA, DEBIN, PIX, and cross-border regulatory compliance.

How long does it take to implement an AI agent system in production?

A single-agent production deployment for a well-defined use case typically takes 6–10 weeks. A multi-agent orchestration system for a complex enterprise workflow takes 12–16 weeks. An enterprise-wide agentic transformation program takes 6–18 months. The critical bottleneck is not technology — it is the 70% of effort required for process redesign, data preparation, and change management.

What is the difference between LangGraph, CrewAI, and Google ADK?

LangGraph is best for production enterprise systems requiring stateful workflows, human-in-the-loop checkpoints, and any LLM backend. Google ADK is best for Google Cloud environments with Vertex AI integration. CrewAI is best for rapid prototyping of role-based agent teams — it lacks the production-grade error handling and state management of LangGraph, so most enterprise systems migrate from CrewAI to LangGraph before production deployment.

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🥉Junior Level • 9 Micro-courses

Agent Engineer Jr

Build Your First AI Agents

Start your journey into AI agents. Master the fundamentals: what agents are, how they work, and build your first functional agent with Claude Code.

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$297

One-time payment • Lifetime access

9 micro-courses

Complete curriculum

1 capstone project

Community access

Prerequisites: Basic programming knowledge (Python or JavaScript)

Curriculum

9 micro-courses + capstone project

1/9

Introduction to AI Agents

What are AI agents, their evolution from rule-based systems to LLM-powered autonomy. The agent revolution and why it matters for your career.

2 hours

2/9

Agent Anatomy

Deep dive into the four pillars: Perception (inputs), Reasoning (LLM core), Action (outputs), and Memory (context). How they orchestrate together.

3 hours

3/9

Types & Classification

Taxonomy of agents: reactive vs deliberative, single vs multi-agent, conversational vs task-oriented, autonomous vs supervised.

2 hours

4/9

Agent Lifecycle

From spawn to termination: initialization, context loading, execution loops, error handling, graceful shutdown, and state persistence.

3 hours

5/9

Intro to Claude Code

Your development environment: installing Claude Code CLI, configuration, project setup, and understanding the agentic coding workflow.

2 hours

6/9

Your First Agent

Hands-on lab: build a functional Q&A agent from scratch. Write your first system prompt, handle user input, and generate responses.

4 hours

7/9

Prompting for Agents

Master agent-specific prompting: role definition, instruction clarity, output formatting, guardrails, and prompt chaining techniques.

3 hours

8/9

Basic Tools

Extend agent capabilities: implement function calling, connect to APIs, file operations, and web search. Build your first tooled agent.

4 hours

9/9

Testing & Deployment

Launch your agent: unit testing patterns, integration testing, local deployment, basic monitoring, and debugging techniques.

3 hours

Capstone Project

🎯 Personal Assistant Agent

Build a complete personal assistant that manages tasks, answers questions using RAG, schedules reminders, and integrates with calendar and email tools.

What You'll Build

Task management

Q&A with context

Calendar integration

Email drafting

8 hours

Hands-on Project

Your Learning Journey

From zero to functional agent in 9 structured micro-courses

Foundation

Modules 1-4

Understand what agents are and how they work

Development

Modules 5-7

Build your first agent with Claude Code

Production

Modules 8-9

Test, deploy, and complete your capstone

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